Bacterial foraging optimization using novel chemotaxis and conjugation strategies

摘要: Bacterial foraging optimization (BFO) has attracted much attention and been widely applied in a variety of scientific engineering applications since its inception. However, the fixed step size lack information communication between bacterial individuals during process have significant impacts on performance BFO. To address these issues real-parameter single objective problems, this paper proposes new optimizer using designed chemotaxis conjugation strategies (BFO-CC). Via mechanism, each bacterium randomly selects standard-basis-vector direction for swimming or tumbling; approach may obviate calculating random unit vector could effectively get rid interfering with other different dimensions. At same time, is adaptively adjusted based evolutionary generations globally best individual, which readily makes algorithm keep better balance local search global search. Moreover, operator employed to exchange individuals; feature can significantly improve convergence. The BFO-CC was comprehensively evaluated by comparing it several competitive algorithms (based swarm intelligence) both benchmark functions real-world problems. Our experimental results demonstrated excellent terms solution quality computational efficiency.